Member of Technical Staff - Software Engineer & Machine Learning at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

09 Mar, 26

Salary

234700.0

Posted On

09 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Platforms, Distributed Systems, AI Insights, Cohort Analysis, Anomaly Detection, Telemetry Ingestion, Secure Workflows, Collaboration, Technical Strategy, Model Evaluation, Observability, Customer Focus, Extensible Code, High-Performance Interfaces, Predictive Insights

Industry

Software Development

Description

Overview As a Member of Technical Staff - Software Engineer & Machine Learning, you will work building AI Insights, a Copilot analytics product that enables our internal stakeholders to move from “What happened?” to “Why did it happen?” in minutes. You’ll design and implement AI-driven trend detection, cohort analysis, and drill-down workflows that connect metrics to real user conversations, developing AI based insights on large scale multi modal Copilot data part of the Microsoft AI (MAI) organization. We’re looking for a experienced Machine Learning engineer with strong hands-on skills in machine learning, data platforms, and distributed systems to lead the development of AI Insights, a next-generation Copilot analytics product. The right candidate takes the initiative and enjoys building world-class consumer experiences and products in a fast-paced environment. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction. Responsibilities Build scalable data pipelines for telemetry ingestion, anomaly detection, and cohort segmentation. Implement ML-driven insights (prompted classifiers, anomaly detection) and integrate them into dashboards and APIs. Develop secure, compliant workflows for handling production logs and conversation data. Enable drill-down capabilities linking quantitative metrics to qualitative evidence for actionable context. Collaborate with PMs and DS to refine hypotheses and deliver intuitive, high-performance interfaces. Own technical strategy for trend detection, cohort analysis, and drill-down workflows linking quantitative metrics to qualitative conversation evidence. Prototype and productionize ML models for anomaly detection and predictive insights. Ensure compliance and security for data handling across telemetry, logs, and conversation datasets. Collaborate with PMs, data scientists, and UX to define roadmap and deliver intuitive, high-impact workflows. Independently write efficient, readable, extensible code and model pipelines. Commit to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer context, and serving as a trusted advisor. Hands-on with observability (metrics, tracing, logs) and model evaluation frameworks. Qualifications Required Qualifications: Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Preferred Qualifications: Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Proven experience leading small engineering and machine learning teams, and collaborating effectively with cross-functional stakeholders including product managers, UX designers, and security specialists. Demonstrated interest in Responsible AI. #MicrosoftAI Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

The role involves building scalable data pipelines and implementing ML-driven insights for a Copilot analytics product. The candidate will also collaborate with product managers and data scientists to refine hypotheses and deliver intuitive interfaces.